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Baghurst P.A.,Womens and Childrens Hospital | Baghurst P.A.,University of Adelaide | Rodbard D.,Biomedical Informatics Consultants LLC | Cameron F.J.,University of Melbourne
Journal of Diabetes Science and Technology | Year: 2010

Aims: While there has been much debate about the clinical importance of glycemic variation (GV), little attention has been directed to the properties of data sets from which it is measured. The purpose of this study is to assess the minimum frequency of glucose measurements from which GV can be consistently and meaningfully measured. Methods: Forty-eight 72 h continuous glucose monitoring traces from children with type 1 diabetes were assessed. Measures of GV included standard deviation (SD), mean amplitude of glycemic excursion (MAGE), and continuous overlapping net glycemic action (CONGA1-4). Measures of GV calculated using 5 min sampling were designated as the 100% or "best estimate" value. Calculations were then repeated for each patient using glucose values spaced at increasing intervals. For each of the specified sampling frequencies, the ratio (%) of the between-subject SD based on the reduced subset of data to the estimate of the SD based on the full 5 min sampling data set was calculated. Results: As the interval between observations increased, so did the variability of the estimators of GV. Standard deviation exhibited the least systematic change at all measurement intervals, and MAGE exhibited the greatest systematic change. Conclusions: In patients with type 1 diabetes, GV as measured by SD or CONGA4, becomes unreliable if observations are more than 2-4 h apart, and estimates of MAGE become unreliable if glucose measurements are more than 1 h apart. MAGE is more unstable and prone to random measurement error than either SD or CONGA. The frequency of glycemic measurements is thus pivotal when selecting a parameter for measurement of GV. © Diabetes Technology Society.


Rodbard D.,Biomedical Informatics Consultants LLC | Vigersky R.A.,U.S. Army
Journal of Diabetes Science and Technology | Year: 2011

Objective: We sought to develop a computerized clinical decision support for clinicians treating patients with type 2 diabetes mellitus (T2DM). Methods: We designed, developed, and tested a computer-assisted decision support (CADS) system using statistical analyses of self-monitoring of blood glucose data, laboratory data, medical and medication history, and individualized hemoglobin A1c goals. A rule-based expert system generated recommendations for changes in therapy and accompanying explanations. Results: A clinical decision support system (CADS) was developed that considers 9 classes of medications and 69 regimens with combinations of up to 4 therapeutic agents. The preferred sequences of regimens can be customized. The program is integrated with a "comprehensive diabetes management system," electronic medical record systems, and a method for uploading data from memory glucose meters via telephone without use of a computer or the Internet. The software provides a report to the clinician regarding the overall quality of glycemic control and identifies problems, e.g., hypoglycemia, hyperglycemia, glycemic variability, and insufficient data. The program can recommend continuation of current therapy, adjustment of dosages of current medications, or change of regimen and can provide explanations for its recommendations. If the user rejects the recommendations, the program will recommend alternative approaches. The CADS also provides access to Food and Drug Administration-approved prescribing information, guidelines from professional organizations, and selections from the general medical literature. The system has been extensively tested with real and synthetic data and is ready for evaluation in multicenter clinical trials. Conclusion: A clinical decision support system to assist with the management of patients with T2DM was designed, developed, tested, and found to perform well. © Diabetes Technology Society.


Rodbard D.,Biomedical Informatics Consultants LLC
Journal of Diabetes Science and Technology | Year: 2015

Background: There is need for readily understandable graphical displays of glucose data to facilitate interpretation by clinicians and researchers. Methods: (1) Display of the percentage of glucose values above a specified threshold for hyperglycemia (%High) versus percentage of glucose values below a specified threshold for hypoglycemia (%Low). If all glucose values fell within the target range, then all data points would fall at the origin. (2) After an intervention, one can plot the change in percentage of glucose values above a specified threshold for hyperglycemia versus the change in percentage of glucose values below a specified threshold defining hypoglycemia: The quadrants of this graph correspond to (a) increased risk of both hyper-And hypoglycemia, (b) decreased hyperglycemia but increased risk of hypoglycemia, (c) decreases in both hypo-And hyperglycemia, and (d) decreased hypoglycemia but increased hyperglycemia. (3) A 2-dimensional triangular graph can be used for simultaneous display of %High, %Low, and percentage in target range. (4) Display of risk of hyper-versus risk of hypoglycemia based on both frequency and severity of departures from the target range can be used. (5) Graphs (1) and (4) can also be presented using percentile scores relative to a reference population. (6) It is also useful to analyze %Hypoglycemia or risk of hypoglycemia versus mean glucose. Results: These methods are illustrated with examples from representative cases and shown to be feasible, practical, and informative. Conclusions: These new types of graphical displays can facilitate rapid analysis of risks of hypo-And hypoglycemia simultaneously and responses to therapeutic interventions for individuals or in clinical trials. © 2015 Diabetes Technology Society.


Rodbard D.,Biomedical Informatics Consultants LLC
Diabetes Technology and Therapeutics | Year: 2013

Bergenstal et al. (Diabetes Technol Ther 2013;15:198-211) described an important approach toward standardization of reporting and analysis of continuous glucose monitoring and self-monitoring of blood glucose (SMBG) data. The ambulatory glucose profile (AGP), a composite display of glucose by time of day that superimposes data from multiple days, is perhaps the most informative and useful of the many graphical approaches to display glucose data. However, the AGP has limitations; some variations are desirable and useful. Synchronization with respect to meals, traditionally used in glucose profiles for SMBG data, can improve characterization of postprandial glucose excursions. Several other types of graphical display are available, and recently developed ones can augment the information provided by the AGP. There is a need to standardize the parameters describing glycemic variability and cross-validate the available computer programs that calculate glycemic variability. Clinical decision support software can identify and prioritize clinical problems, make recommendations for modifications of therapy, and explain its justification for those recommendations. The goal of standardization is challenging in view of the diversity of clinical situations and of computing and display platforms and software. Standardization is desirable but must be done in a manner that permits flexibility and fosters innovation. © Mary Ann Liebert, Inc.


Rodbard D.,Biomedical Informatics Consultants LLC
Postgraduate Medicine | Year: 2011

The practicing physician is faced with the task of interpreting > 2 dozen indices of quality of glycemic control and glycemic variability. It would be desirable to have reference data from relevant patient populations (eg, patients with the same type of diabetes, duration of diabetes, therapeutic regimen, or glycated hemoglobin [HbA1c] levels). The physician can then select the appropriate reference set for interpretation of results for each patient. Institutions and clinics may wish to develop their own reference data. Results can be interpreted as excellent, good, fair, or poor, corresponding with quartiles of their distributions. Each index of glycemic control and variability can be given a numerical score in terms of its percentile within the selected reference population. One can then compute the mean and standard deviation of the percentile scores to obtain an integrated measure of the quality of glycemic control or variability. We calculated quartiles for measures of quality of glycemic control and variability. One can use the percent coefficient of variation (%CV) with criteria that apply irrespective of the HbA1c level as a general rule for interpretation of glycemic variability. For example, a %CV < 33.5% can be regarded as excellent, a %CV between 33.5% to 36.8% as good, a %CV between 36.8% to 40.6% as fair, and a %CV > 40.6% as poor. A graphical display can be used to make more accurate assessments for narrow HbA1c ranges, as the percentiles of the %CV can change systematically with HbA1c level or with mean glucose level. © Postgraduate Medicine.


Rodbard D.,Biomedical Informatics Consultants LLC
Diabetes Technology and Therapeutics | Year: 2012

Aims: We describe a new approach to estimate the risks of hypo- and hyperglycemia based on the mean and SD of the glucose distribution using optional transformations of the glucose scale to achieve a more nearly symmetrical and Gaussian distribution, if necessary. We examine the correlation of risks of hypo- and hyperglycemia calculated using different glucose thresholds and the relationships of these risks to the mean glucose, SD, and percentage coefficient of variation (%CV). Materials and Methods: Using representative continuous glucose monitoring datasets, one can predict the risk of glucose values above or below any arbitrary threshold if the glucose distribution is Gaussian or can be transformed to be Gaussian. Symmetry and gaussianness can be tested objectively and used to optimize the transformation. Results: The method performs well with excellent correlation of predicted and observed risks of hypo- or hyperglycemia for individual subjects by time of day or for a specified range of dates. One can compare observed and calculated risks of hypo- and hyperglycemia for a series of thresholds considering their uncertainties. Thresholds such as 80 mg/dL can be used as surrogates for thresholds such as 50 mg/dL. We observe a high correlation of risk of hypoglycemia with %CV and illustrate the theoretical basis for that relationship. Conclusions: One can estimate the historical risks of hypo- and hyperglycemia by time of day, date, day of the week, or range of dates, using any specified thresholds. Risks of hypoglycemia with one threshold (e.g., 80 mg/dL) can be used as an effective surrogate marker for hypoglycemia at other thresholds (e.g., 50 mg/dL). These estimates of risk can be useful in research studies and in the clinical care of patients with diabetes. © Copyright 2012, Mary Ann Liebert, Inc. 2012.


Rodbard D.,Biomedical Informatics Consultants LLC
Diabetes Technology and Therapeutics | Year: 2016

Continuous glucose monitoring (CGM) provides information unattainable by intermittent capillary blood glucose, including instantaneous real-time display of glucose level and rate of change of glucose, alerts and alarms for actual or impending hypo- and hyperglycemia, "24/7" coverage, and the ability to characterize glycemic variability. Progressively more accurate and precise, reasonably unobtrusive, small, comfortable, user-friendly devices connect to the Internet to share information and are sine qua non for a closed-loop artificial pancreas. CGM can inform, educate, motivate, and alert people with diabetes. CGM is medically indicated for patients with frequent, severe, or nocturnal hypoglycemia, especially in the presence of hypoglycemia unawareness. Surprisingly, despite tremendous advances, utilization of CGM has remained fairly limited to date. Barriers to use have included the following: (1) lack of Food and Drug Administration approval, to date, for insulin dosing ("nonadjuvant use") in the United States and for use in hospital and intensive care unit settings; (2) cost and variable reimbursement; (3) need for recalibrations; (4) periodic replacement of sensors; (5) day-to-day variability in glycemic patterns, which can limit the predictability of findings based on retrospective, masked "professional" use; (6) time, implicit costs, and inconvenience for uploading of data for retrospective analysis; (7) lack of fair and reasonable reimbursement for physician time; (8) inexperience and lack of training of physicians and other healthcare professionals regarding interpretation of CGM results; (9) lack of standardization of software methods for analysis of CGM data; and (10) need for professional medical organizations to develop and disseminate additional clinical practice guidelines regarding the role of CGM. Ongoing advances in technology and clinical research have addressed several of these barriers. Use of CGM in conjunction with an insulin pump with automated suspension of insulin infusion in response to actual observed or predicted hypoglycemia, as well as progressive refinement of closed-loop systems, is expected to dramatically enhance the clinical utility and utilization of CGM. © Copyright 2016, Mary Ann Liebert, Inc. 2016.


PubMed | Biomedical Informatics Consultants LLC
Type: Comparative Study | Journal: Journal of diabetes science and technology | Year: 2014

There is need for readily understandable graphical displays of glucose data to facilitate interpretation by clinicians and researchers. (1) Display of the percentage of glucose values above a specified threshold for hyperglycemia (%High) versus percentage of glucose values below a specified threshold for hypoglycemia (%Low). If all glucose values fell within the target range, then all data points would fall at the origin. (2) After an intervention, one can plot the change in percentage of glucose values above a specified threshold for hyperglycemia versus the change in percentage of glucose values below a specified threshold defining hypoglycemia: The quadrants of this graph correspond to (a) increased risk of both hyper- and hypoglycemia, (b) decreased hyperglycemia but increased risk of hypoglycemia, (c) decreases in both hypo- and hyperglycemia, and (d) decreased hypoglycemia but increased hyperglycemia. (3) A 2-dimensional triangular graph can be used for simultaneous display of %High, %Low, and percentage in target range. (4) Display of risk of hyper- versus risk of hypoglycemia based on both frequency and severity of departures from the target range can be used. (5) Graphs (1) and (4) can also be presented using percentile scores relative to a reference population. (6) It is also useful to analyze %Hypoglycemia or risk of hypoglycemia versus mean glucose. These methods are illustrated with examples from representative cases and shown to be feasible, practical, and informative. These new types of graphical displays can facilitate rapid analysis of risks of hypo- and hypoglycemia simultaneously and responses to therapeutic interventions for individuals or in clinical trials.


PubMed | Biomedical Informatics Consultants LLC
Type: Journal Article | Journal: Diabetes technology & therapeutics | Year: 2012

We describe a new approach to estimate the risks of hypo- and hyperglycemia based on the mean and SD of the glucose distribution using optional transformations of the glucose scale to achieve a more nearly symmetrical and Gaussian distribution, if necessary. We examine the correlation of risks of hypo- and hyperglycemia calculated using different glucose thresholds and the relationships of these risks to the mean glucose, SD, and percentage coefficient of variation (%CV).Using representative continuous glucose monitoring datasets, one can predict the risk of glucose values above or below any arbitrary threshold if the glucose distribution is Gaussian or can be transformed to be Gaussian. Symmetry and gaussianness can be tested objectively and used to optimize the transformation.The method performs well with excellent correlation of predicted and observed risks of hypo- or hyperglycemia for individual subjects by time of day or for a specified range of dates. One can compare observed and calculated risks of hypo- and hyperglycemia for a series of thresholds considering their uncertainties. Thresholds such as 80 mg/dL can be used as surrogates for thresholds such as 50 mg/dL. We observe a high correlation of risk of hypoglycemia with %CV and illustrate the theoretical basis for that relationship.One can estimate the historical risks of hypo- and hyperglycemia by time of day, date, day of the week, or range of dates, using any specified thresholds. Risks of hypoglycemia with one threshold (e.g., 80 mg/dL) can be used as an effective surrogate marker for hypoglycemia at other thresholds (e.g., 50 mg/dL). These estimates of risk can be useful in research studies and in the clinical care of patients with diabetes.


PubMed | Biomedical Informatics Consultants LLC
Type: | Journal: Diabetes technology & therapeutics | Year: 2016

Continuous glucose monitoring (CGM) provides information unattainable by intermittent capillary blood glucose, including instantaneous real-time display of glucose level and rate of change of glucose, alerts and alarms for actual or impending hypo- and hyperglycemia, 24/7 coverage, and the ability to characterize glycemic variability. Progressively more accurate and precise, reasonably unobtrusive, small, comfortable, user-friendly devices connect to the Internet to share information and are sine qua non for a closed-loop artificial pancreas. CGM can inform, educate, motivate, and alert people with diabetes. CGM is medically indicated for patients with frequent, severe, or nocturnal hypoglycemia, especially in the presence of hypoglycemia unawareness. Surprisingly, despite tremendous advances, utilization of CGM has remained fairly limited to date. Barriers to use have included the following: (1) lack of Food and Drug Administration approval, to date, for insulin dosing (nonadjuvant use) in the United States and for use in hospital and intensive care unit settings; (2) cost and variable reimbursement; (3) need for recalibrations; (4) periodic replacement of sensors; (5) day-to-day variability in glycemic patterns, which can limit the predictability of findings based on retrospective, masked professional use; (6) time, implicit costs, and inconvenience for uploading of data for retrospective analysis; (7) lack of fair and reasonable reimbursement for physician time; (8) inexperience and lack of training of physicians and other healthcare professionals regarding interpretation of CGM results; (9) lack of standardization of software methods for analysis of CGM data; and (10) need for professional medical organizations to develop and disseminate additional clinical practice guidelines regarding the role of CGM. Ongoing advances in technology and clinical research have addressed several of these barriers. Use of CGM in conjunction with an insulin pump with automated suspension of insulin infusion in response to actual observed or predicted hypoglycemia, as well as progressive refinement of closed-loop systems, is expected to dramatically enhance the clinical utility and utilization of CGM.

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